Volleyball Data Analysis

This is an R Markdown Notebook. Each section of the code is then explained.

First of all import the libraries needed

#install.packages(c("datavolley", "ovlytics"))
library(datavolley)
library(ggplot2)
library(dplyr)
library(ovlytics)

Import the file you are interested in

#filename <- "C:/Users/mirko/OneDrive - Politecnico di Milano/Altro/Volley/Conco2324/Parella Torino/Ritorno/&09_VOL-LIB_Libellula_b1fa_23_s.dvw"
d <- dir("C:/Users/mirko/OneDrive - Politecnico di Milano/Altro/Volley/Conco2324/Parella Torino/Ritorno/", pattern = "dvw$", full.names = TRUE)

lx <- list()
## read each file
for (fi in seq_along(d)) lx[[fi]] <- dv_read(d[fi], insert_technical_timeouts = FALSE)
## now extract the play-by-play component from each and bind them together
px <- list()
for (fi in seq_along(lx)) px[[fi]] <- plays(lx[[fi]])
#plays(lx[[1]])
px <- do.call(rbind, px)

Now let’s focus on attack rate. CHANGE THE ’‘’teamName’’’ variable according to the match you are interested in

attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName) %>%
  group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
  mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup

`summarise()` has grouped output by 'team'. You can override using the `.groups` argument.

## add x, y coordinates associated with the zones
attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))

## for team 2, these need to be on the top half of the diagram
tm2i <- attack_rate$team == teams(px)[2]
attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])

ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
    scale_fill_gradient2(name = "Attack rate")

Now let’s plot the heatmap of where attacks ended

If you want a specific vector plot of attakcs directions:

playerName = 'ALESSIA DAMATO'
## first tabulate attacks by starting and ending zone
attack_rate <- px %>% dplyr::filter(team == teams(px)[2] & skill == "Attack" & team == teamName & player_name == playerName & start_zone == 4) %>%
  group_by(start_zone, end_zone) %>% tally() %>% ungroup

## convert counts to rates
attack_rate$rate <- attack_rate$n/sum(attack_rate$n)

## discard zones with zero attacks or missing location information
attack_rate <- attack_rate %>% dplyr::filter(rate>0 & !is.na(start_zone) & !is.na(end_zone))

## add starting x, y coordinates
attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower", xynames = c("sx", "sy")))

## and ending x, y coordinates
attack_rate <- cbind(attack_rate, dv_xy(attack_rate$end_zone, end = "upper", xynames = c("ex", "ey")))

## plot in reverse order so largest arrows are on the bottom
attack_rate <- attack_rate %>% dplyr::arrange(desc(rate))

p <- ggplot(attack_rate, aes(x, y, col = rate)) + ggcourt(labels = c(teams(px)[2], ""), court_colour = "indoor")
for (n in 1:nrow(attack_rate))
    p <- p + geom_path(data = data.frame(x = c(attack_rate$sx[n], attack_rate$ex[n]),
                                         y = c(attack_rate$sy[n], attack_rate$ey[n]),
                                         rate = attack_rate$rate[n]),
                       aes(size = rate), lineend = "round",
                       arrow = arrow(length = unit(2, "mm"), type = "closed", angle = 20, ends = "last"))
p + scale_colour_gradient(name = "Attack rate") + guides(size = "none")

Reception plots

px %>% dplyr::filter(skill == "Reception", team == teamName, end_zone == 5) %>% group_by(player_name) %>% 
  dplyr::summarize(N_receptions = n(), error_rate = sum(evaluation_code == "=",na.rm = TRUE))

Sankey Diagram for Reception

This diagram is useful to detect the direction the other team suffers more.

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